灰色GM(1,1)模型在南京居民消费预测中的应用

灰色系统理论的研究对象是"部分信息已知,部分信息未知"的"小样本"、"贫信息"不确定性系统。它通过对"部分"已知信息的生成、开发去了解、认识现实世界,实现对系统运行行为和演化规律的正确把握和描述。GM(1,1)灰色模型是其中应用最为广泛的一个模型。本文以2003～2007年南京地区城市居民消费数据为依据,建立了南京市2003～2007年城市居民人均消费支出的GM(1,1)灰色模型,并应用该模型对南京市城市居民人均消费变化趋势做了预测,预计2009年南京地区城市居民消费为18093元。     

非线性GM（1，1,α）模型在港口吞吐量预测中的应用

针时统计数据缺乏的预测问题,提出引入灰色系统理论来解决,以连云港2003—2007年港口吞吐量的数据为基础建立了非线性灰色GM（1,1,α）模型,详细阐述了灰色预测法的应用过程。结果表明,非线性灰色模型对样本的适应性强,预测精度高,简单易行,能够有效解决港口吞吐量预测问题。     

基于灰色理论的铜陵市原煤需求预测分析

根据灰色系统理论,结合铜陵市2001-2007年原煤消费量的数据,建立铜陵市原煤消费量数列的灰色预测模型,运用灰色预测GM(1,1)模型及matlab工具预测铜陵市2008-2015年的原煤消费需求量,并对预测结果予以分析.     

基于灰色系统理论的湖南省物流需求预测分析

文中以灰色系统理论思想为指导,运用灰色GM(1,1)模型,以货运量表征物流规模。根据湖南省近10年来的物流需求变化,建立了灰色预测的GM(1,1)模型,预测湖南省今后5年的物流需求,为政府规划地区物流发展提供相关理论参考。     

陕西省地方电力需求的灰色分析与预测

基于陕西省地方电力（集团）公司1996-2005年期间的电力需求资料,根据灰色系统理论,建立灰色GM（1,1）预测模型,动态预测省地方电力系统“十一五”期间电力需求量,为电力发展规划提供科学决策依据。     

基才灰色生产函数模型和索洛“余值法”的广西技术进步贡献率测度研究

本文采用灰色系统理论的思想和模型技术,在C-D生产函数和“索洛”模型的基础上,结合动态灰色GM（1,1）模型,对原始数据施以缓冲算子,用GM（1,1）模拟值建立灰色生产函数模型。此方法有效解决了测算期内非技术进步因素的不利影响。测量和分析了广西“七五”至“十一五”规划期间经济增长中技术进步的贡献率,并分时段分析了技术、资本、劳动三者对经济增长贡献率的变化,进而为促进广西经济的发展提供科学依据。研究表明,广西经济增长中资本投入的贡献率最大,平均达到49．29％,是维持经济高速增长的最主要因素;其次是科技进步39．25％,劳动增长贡献率最低,仅有11．47％。     

信息在线

新闻链接我国城市居民消费结构发生重大质变(一)住房、汽车和通讯网络器材成为城市居民生活中的“新三大件”其中,人均住房消费支出为7267.94元,占居民消费支出的53.5%,位居商品消费支出的首位;人均汽车消费支出为891.57元,占居民消费支出的6.5%,位居商品消费支出的第二位;人均通讯网络器材支出为488.86元,占居民消费支出的3.6%,位居商品支出的第三位。(二)教育、医疗和旅游成为新世     

灰色系统模型的热计量表市场需求预测研究

本文根据热计量表市场的特性,采用灰色系统理论对市场需求进行预测,建立灰色系统GM（1,1）模型,并通过残差检验的方法验证了模型的正确性和较高的精度。基于热计量表行业2006年到2012年的销售量来对未来五年的需求加以预测。     

组合灰色预测模型对物流需求预测效果的分析

在线性回归与GM（1,1）组合模型对物流需求量预测,可取得优于单方法预测效果的前提下,将线性回归法运用到与DGM（1,1）模型进行组合,检测该组合灰色预测模型对物流需求量预测效果。通过对国内2001-2012年物流需求量历史数据的分析,发现组合灰色预测模型的预测效果优于线性回归模型,但低于DGM（1,1）模型,最后利用线性回归模型、DGM（1,1）模型、简单平均组合模型和组合灰色预测模型对国内2013-2017的物流需求量进行了有效预测。     

基于灰色系统理论的冀西北地区物流需求预测分析

本文以灰色系统理论思想为指导，运用灰色GM（1，1）模型，以货运量表征物流规模。根据冀西北地区近10年来的物流需求变化，建立了灰色预测的GM（1，1）模型，预测冀西北地区今后5年的物流需求，为政府规划地区物流发展提供相关理论参考。     

Productivity,R&D Spillovers and Educational Attainment*

Economists have long agreed that the local availability of a more qualified workforce generates significant spillovers. This study suggests that these externalities may arise because plants by having access to a more qualified workforce at a regional level, can benefit more from R&D spillovers than those located in areas with less qualified workforce. This hypothesis is tested on a sample of British establishments drawn from the Annual Business Inquiry over the period 1997–2002. The main results are consistent with our expectations that the regional differences in the industry‐level educational attainment of the workforce available to a plant will condition its capability of absorbing R&D spillovers.     

灰色预测法在经济预测中的应用

客观世界,既是物质的世界又是信息的世界。它既包含大量的己知信息,也包含大量的未知信息与非确知信息。未知的或非确知的信息称为黑色信息;己知信息称为白色信息。既含有己知信息又含有未知的、非确知的信息的系统,称为灰色系统。本文主要利用灰色系统理论进行了应用研究,进一步证明了灰色系统理论在现实中的应用价值。     

用改进的蒙特卡罗模拟估计我国商业银行操作风险

运用蒙特卡罗模拟方法估计银行业操作风险时,对于事件发生频率,一般不考虑预测模型参数的时变性,致使预测结果存在较大偏差。针对这一不足,建立了灰色动态残差GM(1,1)模型来估计与预测损失事件的发生频率,并通过对起始时点的比较选择和残差的修正,进一步改进了预测模型。再用蒙特卡罗方法对操作风险的损失金额进行模拟,配合使用所建立的损失事件发生频率预测模型,得到商业银行操作风险的损失值,并据以确定监管资本和减少商业银行操作风险的对策。     

Primary prevention and health outcomes: Treatment of residential lead-based paint hazards and the prevalence of childhood lead poisoning

In order to gain a better understanding of the effects of an investment in primary prevention on health, I investigate the impact of treatment of lead-based paint hazards in housing units (the preventive action) on childhood lead poisoning (the health outcome) at the census tract level in Chicago, IL. I use the findings from the analysis to simulate and then weigh the costs of lead interventions against the potential benefits of reducing blood lead levels in children. Childhood lead poisoning presents an interesting case study of the potential of preventive care in reducing the prevalence of a disease. There is a clear, well-defined pathway of exposure (deteriorating lead paint in older homes) and no method of secondary care that effectively mitigates the negative health effects. I find that a one-tenth percentage point increase in the proportion of older housing units that have been remediated is associated with a four-tenths percentage point reduction in the prevalence of childhood lead poisoning, an elasticity of roughly 0.5. Citywide, this is roughly 2.5 cases of lead poisoning averted for every housing unit remediated. Furthermore, I find evidence that the effect of remediations in preventing the disease has improved over time. The lower bound estimates of the benefits associated with the reduction in lead poisoning - increased expected lifetime earnings and reduced medical expenditures - are two to twenty times the estimated costs of the remediations.     

Congestion in a city with a central bottleneck

We consider dynamic congestion in an urban setting where trip origins are spatially distributed. All travelers must pass through a downtown bottleneck in order to reach their destination in the CBD. Each traveler chooses departure time to maximize general concave scheduling utility. We find that, at equilibrium, travelers sort according to their distance to the destination; the queue is always unimodal regardless of the spatial distribution of trip origins. We construct a welfare maximizing tolling regime, which eliminates congestion. All travelers located beyond a critical distance from the CBD gain from tolling, even when toll revenues are not redistributed, while nearby travelers lose. We discuss our results in the context of acceptability of tolling policies.     

A reduced form framework for modeling volatility of speculative prices based on realized variation measures

Building on realized variance and bipower variation measures constructed from high-frequency financial prices, we propose a simple reduced form framework for effectively incorporating intraday data into the modeling of daily return volatility. We decompose the total daily return variability into the continuous sample path variance, the variation arising from discontinuous jumps that occur during the trading day, as well as the overnight return variance. Our empirical results, based on long samples of high-frequency equity and bond futures returns, suggest that the dynamic dependencies in the daily continuous sample path variability are well described by an approximate long-memory HAR–GARCH model, while the overnight returns may be modeled by an augmented GARCH type structure. The dynamic dependencies in the non-parametrically identified significant jumps appear to be well described by the combination of an ACH model for the time-varying jump intensities coupled with a relatively simple log-linear structure for the jump sizes. Finally, we discuss how the resulting reduced form model structure for each of the three components may be used in the construction of out-of-sample forecasts for the total return volatility.     

Estimation of objective and risk-neutral distributions based on moments of integrated volatility

In this paper, we present an estimation procedure which uses both option prices and high-frequency spot price feeds to estimate jointly the objective and risk-neutral parameters of stochastic volatility models. The procedure is based on a method of moments that uses analytical expressions for the moments of the integrated volatility and series expansions of option prices and implied volatilities. This results in an easily implementable and rapid estimation technique. An extensive Monte Carlo study compares various procedures and shows the efficiency of our approach. Empirical applications to the Deutsche mark–US dollar exchange rate futures and the S&P 500 index provide evidence that the method delivers results that are in line with the ones obtained in previous studies where much more involved estimation procedures were used.     

A zero-inflated ordered probit model,with an application to modelling tobacco consumption

Data for discrete ordered dependent variables are often characterised by “excessive” zero observations which may relate to two distinct data generating processes. Traditional ordered probit models have limited capacity in explaining this preponderance of zero observations. We propose a zero-inflated ordered probit model using a double-hurdle combination of a split probit model and an ordered probit model. Monte Carlo results show favourable performance in finite samples. The model is applied to a consumer choice problem of tobacco consumption indicating that policy recommendations could be misleading if the splitting process is ignored.     

Econometric specification of stochastic discount factor models

We consider the problem of derivative pricing when the stochastic discount factors are exponential-affine functions of underlying state variable. In particular we discuss the conditionally Gaussian framework and introduce semi-parametric pricing methods for models with path dependent drift and volatility. This approach is also applied to more complicated frameworks, such as pricing of a derivative written on an index, when the interest rate is stochastic.